Extrapolating multi‐decadal plant community changes based on medium‐term experiments can be risky: evidence from high‐latitude tundra

Abstract

For most experimental studies the short‐term responses to manipulation often differ from the long‐term changes in the community composition, dynamics or functioning. Such discrepancy limits the translation of experimental results into key ecological topics such as global environmental change. Here we analyzed plant community dynamics from a 23‐year transplant experiment in the Fennoscandian mountain tundra and explored how well the pattern of responses over the first 12 years of the experiment can predict longer‐term changes. Sod‐blocks of tundra heath vegetation were transplanted to a snowbed 150 m higher in elevation from their origin, where, with contrasting levels of soil wetness, half of the transplants were protected from mammalian herbivores. Throughout the experiment, community changes strongly depended on both plant functional types and experimental treatments. The first 12 years were characterized by a response to transplantation to the snowbed showing a strong increase of graminoid and a decrease of shrub abundances in the transplants. In the longer term, the community divergence increased in particular in response to grazing and soil wetness within the snowbed, while graminoid dominance disappeared. Markov chain models captured the main trends during the first 12 years but they failed to predict their relative abundance after 23 years. In particular, the late dominance of bryophytes in the wet snowbed, the recovery of shrubs in the dry exclosures, and the subordinate status of graminoids deviated from the extrapolation based on the medium‐term trends. Despite clear community dynamical trajectories detected in the first decade, the differences in the temporal scale of both treatment effects and plant functional type responses limited their ability to extrapolate longer‐term trajectories. We find that increasing focus on long‐term experiments is a crucial step to understanding the processes involved in the response of plant communities to global environmental change.

Open tundra persist, but arctic features decline—Vegetation changes in the warming Fennoscandian tundra

“…Periodically intense vertebrate herbivory belongs to the natural state of the tundra biome, (Batzli, White, McLean, Pitelka, & Collier, ; Oksanen & Olofsson, ; Oksanen & Virtanen, ; Olofsson et al., ; Saccone & Virtanen, ; Tihomirov, ; Virtanen, ) and the impact of climate change on the arctic vegetation is always modified by herbivore–plant interactions. Thus, changing herbivore impacts have to be considered when interpreting the results.…”

What if plant functional types conceal species‐specific responses to environment? Study on arctic shrub communities

“…). Moreover, in these very low‐productivity environments, the detection of biotic interactions can require experimentation on decadal time scales (Saccone and Virtanen ). It is likely that, in such habitats, the effect of neighboring shrubs in terms of litter production (Buckeridge et al.…”

A reality check for climate change experiments: Do they reflect the real world?

“…Experiments also suffer from issues of scale (Sandel and Smith ) and tend to focus on short‐term mechanisms that may not drive longer‐term outcomes (Strauss et al. , Saccone and Virtanen ). Despite these and other well‐known limitations (Tilman ), we rely on climate change experiments to provide insight about the future, and in many cases to parameterize and improve models integral for forecasting (Luo et al.…”

“…Experiments, for example, often manipulate key ecological drivers in a simplistic manner, failing to capture the complexity of the real world (Kreyling and Beier 2013). Experiments also suffer from issues of scale (Sandel and Smith 2009) and tend to focus on short-term mechanisms that may not drive longer-term outcomes (Strauss et al 2008, Saccone andVirtanen 2016). Despite these and other wellknown limitations (Tilman 1989), we rely on climate change experiments to provide insight about the future, and in many cases to parameterize and improve models integral for forecasting (Luo et al 2011a, b).…”